BASELINE METABOLIC PROFILING AND RISK OF DEATH FROM COVID-19

ObjectiveTo derive a predicted probability of death (PDeathLabs) based upon complete value sets for 11 clinical measurements (CM) obtained on patients prior to their diagnosis of coronavirus disease (COVID-19). PDeathLabs is intended for use as a summary metric for baseline metabolic status in multivariate models for COVID-19 death. MethodsCases were identified through the COVID-19 Shared Data Resource (CSDR) of the Department of Veterans Affairs. The diagnosis required at least one positive nucleic acid amplification test (NAAT). The primary outcome was death within 60 days of the first positive test. We retrieved all values for systolic blood pressure (SBP), diastolic blood pressure (DBP), oxygen saturation (O2SAT), body mass index (BMI), estimated glomerular filtration rate (EGFR), alanine aminotransferase (ALT), serum albumin (ALB), hematocrit (HCT), LDL cholesterol (LDL) hemoglobin A1c (A1C), and HDL cholesterol (HDL) if they were done at least 14 days prior to the NAAT. Clinicians evaluate several attributes of CM that are of critical importance: metabolic control, disease burden, chronicity, refractoriness, tendency to relapse, temporal trends, and lability. We derived 1-3 parameters for each of these attributes: the most recent value (metabolic control); time-weighted average and abnormal area under a severity versus time curve (disease burden); time and number of readings above or below goal (chronicity); longest abnormal cluster and time/number of consecutive readings above goal if the last value was abnormal (refractoriness); number of abnormal clusters (tendency to relapse); long- and short-term changes (temporal trends); and coefficient of variation and mean deviation between consecutive readings (lability). We created computer programs to derive cumulative values for these 13 parameters for all 11 CM as each new value is added. A fitted logistic model was developed for each CM to determine which of the 13 parameters contributed to the risk of death. A main logistic model was developed to determine which of the 13 x 11 = 143 metabolic parameters were independently predictive of death. The resulting model was used to derive PDeathLabs for each patient and the area under its receiver operating characteristic (ROC) curve calculated. Single variable logistic models were also derived for age at diagnosis, the Charlson 2-year (Charl2Yr) and lifetime (CharlEver) scores, and the Elixhauser 2-year (Elix2Yrs) and lifetime (ElixEver) scores. Stata was used to compare the ROCs for PDeathDx and each of the other metrics. ResultsOn September 30, 2021, there were 347,220 COVID-19 patients in the CSDR. 329,491 (94.9%) patients had CM performed at least 14 days prior to the COVID-19 diagnosis and form the basis for this report. 17,934 (5.44%) died within 60 days of the diagnosis. On the subset regressions, the number of significant parameters ranged from all 13 for SBP to 7 for HDL. 239,393 patients had complete sets of data for developing the main model. Of 143 candidate predictors, 49 parameters were identified as statistically significant, independent predictors of death. The most influential domains were the most recent value, disease burden, temporal trends, and tendency to relapse. The ROC area for PDeathLabs was 0.785 +/- 0.002. No difference was found in the ROC areas of PDeathLabs and age at diagnosis (0.783 +/- 0.002; P = NS). However, the ROC area for PDeathLabs was significantly greater than that of Charl2Yrs (0.704 +/- 0.002; P < 0.001), CharlEver (0.729 +/- 0.002; P < 0.001), Elix2Yrs (0.675 {+/-} 0.002; P < 0.001), and ElixEver (0.707 +/- 0.002; P < 0.001). A poor prognosis was found for chronic systolic hypertension. On the other hand, a higher BMI was protective once SBP, DBP, HDL, LDL and A1C were considered. ConclusionsOur study confirms that parameters derived for 11 CM are significant determinants of COVID-19 death. The most recent value should not be selected over other parameters for multivariate modeling unless there is a physiologic basis for doing so. PDeathLabs has the same discriminating power as age at diagnosis and outperforms comorbidity indices as a summary metric for pre-existing conditions. If validated by others, this approach provides a robust approach to handling CM in multivariate models.

A NOVEL METHOD FOR HANDLING PRE-EXISTING CONDITIONS IN PREDICTION MODELS FOR COVID-19 DEATH

ObjectiveTo derive a predicted probability of death (PDeathDx) based upon complete sets of ICD-10 codes assigned to patients prior to their diagnosis of COVID-19. PDeathDx is intended for use as a summary metric for pre-existing conditions in multivariate models for COVID-19 death. MethodsCases were identified through the COVID-19 Shared Data Resource (CSDR) of the Department of Veterans Affairs. The diagnosis required at least one positive nucleic acid amplification test (NAAT). The primary outcome was death within 60 days of the first positive test. We retrieved all diagnoses entered into the electronic medical record for visits, on problem lists, and at the time of hospital discharge if they were at least 14 days prior to the NAAT. ICD-9 codes were converted to ICD-10 equivalents using a crosswalk provided by the Centers for Medicare/Medicaid Services. ICD-10 codes were converted to their category diagnoses defined as all columns to the left of the decimal point. Each patient was considered to have or not have each category diagnosis prior to the NAAT. A computer program calculated the number of cases for each category diagnosis, the relative risk (RR) of death, and its confidence interval (CI) using a Bonferroni adjustment for multiple comparisons. RRs were re-centered by subtracting 1 so that high-risk conditions had a positive value while protective conditions had a negative one. Diagnoses found to be significant were entered into a logistic model for death in a stepwise fashion. Each patient was assigned (RR-1) to each category diagnosis if they had the condition or 0 otherwise. The resulting model was used to derive PDeathDx for each patient and the area under its receiver operating characteristic (ROC) curve calculated. Single variable logistic models were also derived for age at diagnosis, the Charlson 2-year (Charl2Yr) and lifetime (CharlEver) scores, and the Elixhauser 2-year (Elix2Yrs) and lifetime (ElixEver) scores. Stata was used to compare the ROCs for PDeathDx and each of the other metrics. ResultsOn September 30, 2021 there were 347,220 COVID-19 patients in the CSDR. 18,120 patients (5.33%) died within 60 days of their diagnosis. After consolidating ICD-9 and ICD-10 codes, 29,162,710 separate diagnoses were given to the subjects representing 41,341 ICD-10 codes. This set was reduced to 1,890 category diagnoses assigned to the group for the first time on 19,184,437 occasions. Of the 1,890 category diagnoses, 425 involved >= 100 subjects and had a lower boundary for the CI >= 1.50 (a high-risk condition) or upper boundary <= 0.80 (a protective condition). Stepwise logistic regression showed that 153 were statistically significant, independent predictors of death. PDeathDx was slightly less powerful than age as a discriminator (ROC = 0.811 +/- 0.002 vs 0.812 +/- 0.001, respectively; P < 0.001) but was superior to the Charl2Yr (ROC = 0.727 +/- 0.002; P < 0.001), CharlEver (ROC = 0.753 +/- 0.002; P <= 0.001), Elix2Yr (ROC = 0.694 +/- 0.002; P < 0.001); and ElixEver (ROC = 0.731 +/- 0.002; P < 0.001). Univariate analysis and multivariate modeling showed that many of the most high-risk conditions are under-represented or not included in the Charlson Index. These include hypertension, dementia, degenerative neurologic disease, or diagnoses associated with severe physical disability. ConclusionsOur method for handling pre-existing conditions in multivariate analysis has many advantages over conventional comorbidity indices. The approach can be applied to any condition or outcome, can use any categorical predictors including medications, creates its own condition weights, handles rare as well as protective conditions, and returns actionable information to providers. The latter include the specific ICD-10 groups, their contribution to the risk, and their rank order of importance. Finally, PDeathDx is equivalent to age as a discriminator of outcomes and outperforms 4 other comorbidity scores. If validated by others, this approach provides an alternative and more robust approach to handling comorbidities in multivariate models.

ESTIMATING THE EFFECT OF VACCINATION ON THE CASE-FATALITY RATE FOR COVID-19

ObjectiveTo evaluate the benefits of vaccination on the case fatality rate (CFR) for COVID-19 infections. DesignMultivariate modeling of data from electronic medical records Setting130 medical centers of the United States Department of Veterans Affairs Participants339,772 patients with COVID-19 confirmed by nucleic acid amplification testing as of September 30, 2021 MethodsThe primary outcome was death within 60 days of the diagnosis. Patients were considered vaccinated if they had completed a full series >= 14 days prior to diagnosis. Cases presenting in July - September of 2021 were considered to have the delta variant. Logistic regression was used to derive adjusted odds ratios (OR) for vaccination and infection with delta versus earlier variants. Models were adjusted for demographic traits, standard comorbidity indices, selected clinical terms, and 3 novel parameters representing all prior diagnoses, all prior vital signs/ baseline laboratory tests, and current outpatient treatment. Patients with a delta infection were divided into 8 cohorts based upon the time from vaccination to diagnosis (in 4-week blocks). A common model was used to estimate the odds of death associated with vaccination for each cohort relative that of all unvaccinated patients. Results9.1% of subjects had been fully vaccinated, and 21.5% were presumed to have the delta variant. 18,120 patients (5.33%) died within 60 days of their diagnoses. The adjusted OR for delta infection was 1.87 +/- 0.05 which corresponds to a relative risk of 1.78. The overall adjusted OR for prior vaccination was 0.280 +/- 0.011 corresponding to a relative risk of 0.291. The study of vaccine cohorts with a delta infection showed that the raw CFR rose steadily after 10-14 weeks. However, the OR for vaccination remained stable for 10-34 weeks. ConclusionsOur study confirms that delta is substantially more lethal than earlier variants and that vaccination is an effective means of preventing COVID death. After adjusting for major selection biases, we found no evidence that the benefits of vaccination on CFR declined over 34 weeks.